Prior to a short discussion of the related art being set forth, it may be helpful to set forth definitions of certain terms that will be used hereinafter.
The term “mobile device” refers herein to any device used directly by an end-user to communicate. It can be a hand-held telephone, a laptop computer equipped with a mobile broadband adapter, or any other device. In the context used herein, mobile device refers specifically to an arbitrary platform which is equipped with image capturing, image processing, and wireless communication capabilities.
The term “testing dipstick” or simply “dipstick” refers herein to a testing measurement device usually made of paper or cardboard and is impregnated with reagents that indicate some feature of a liquid or a gas by changing color. In medicine, dipsticks can be used to test for a variety of liquids for the presence of a given substance, known as an analyte. For example, urine dipsticks are used to determine properties of a given sample and detect and measure the presence of a variety of substances that indicate a person's state of health.
The term “specularity” refers herein to the visual appearance of specular reflection. In computer vision, it means the mirror like properties of the surface: A directional reflection of incoming light (illumination) as described by the law of reflection. A simplified modeling of that reflection is the specular component in the Phong reflection model.
Dipsticks are used by a variety of healthcare providers to assist in diagnostics, specifically, but not exclusively of urinary analysis of patients. The core concept is a set of reagents which are designed to chemically react to substances in a liquid under test (e.g., urine) by changing their color within a predefined color range. The set of colored reagents can then be compared to a predefined color key which can be used, either manually (e.g., by an expert user) or automatically (e.g., using a dedicated image processing computerized system) to yield qualitative and quantitative data relating to the substances in the liquid under test.
Currently, computer vision can be used to interpret the color reagent responses into quantitative and qualitative clinical data. This is being carried out by dedicated hardware which may include a pre-calibrated scanner, which is operated in well-known and monitored illumination conditions, and a classifier that operates based on the calibrated images derived by the scanner.
The need to use dedicated hardware necessitates patients carry out the dipstick test in clinics rather than in the convenience of their home or other place of choice. Such a visit to the lab also mandates coming in unnecessary contact with infections and diseases. A non-expert interpretation of the dipstick is also not recommended—for the fear of wrong interpretation and misdiagnosis. It would, therefore, be advantageous to be able to produce such accurate clinical data at home, using image processing techniques, without the need to use a dedicated hardware or software.